12 Exploratory analysis and visualization with ACS data
12.1 Identifying and visualizing the counties from each state with the highest median income
highest_income_counties<-median_income %>%
separate(NAME,c("County","State"),sep=",") %>%
group_by(State) %>%
arrange(desc(median_income)) %>%
slice_max(median_income, n=1) %>%
unite(NAME, c("County","State"), remove=FALSE, sep=",")
highest_income_counties## # A tibble: 52 x 7
## # Groups: State [52]
## GEOID NAME County State variable median_income moe
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 01117 Shelby County, Alabama Shelby County " Alabama" B19013_0… 77799 2248
## 2 02110 Juneau City and Borough, Alas… Juneau City and B… " Alaska" B19013_0… 88390 4059
## 3 04013 Maricopa County, Arizona Maricopa County " Arizona" B19013_0… 64468 326
## 4 05007 Benton County, Arkansas Benton County " Arkansas" B19013_0… 66362 1292
## 5 06085 Santa Clara County, California Santa Clara County " California" B19013_0… 124055 1117
## 6 08035 Douglas County, Colorado Douglas County " Colorado" B19013_0… 119730 1710
## 7 09001 Fairfield County, Connecticut Fairfield County " Connecticut" B19013_0… 95645 1039
## 8 10003 New Castle County, Delaware New Castle County " Delaware" B19013_0… 73892 1210
## 9 11001 District of Columbia, Distric… District of Colum… " District of C… B19013_0… 86420 1008
## 10 12109 St. Johns County, Florida St. Johns County " Florida" B19013_0… 82252 2741
## # … with 42 more rowshighest_income_counties_viz<-
highest_income_counties %>%
ggplot(aes(x=median_income,y=reorder(NAME, median_income)))+
geom_errorbarh(aes(xmin = median_income - moe, xmax = median_income + moe)) +
geom_point(color = "red",
size = 3)+
labs(title="County with Highest Median Income, by State",
y="",
x="Median Income Estimate from 5-year ACS\n(bars indicate margin of error)")+
theme(plot.title=element_text(hjust=0.5))highest_income_counties_viz
12.2 Visualizing county-level median income for Colorado
median_income_CO_counties_2018<-get_acs(geography="county",
state="CO",
variables="B19013_001",
year=2018) %>%
rename(median_income=estimate) %>%
arrange(desc(median_income))## Getting data from the 2014-2018 5-year ACS## Using FIPS code '08' for state 'CO'median_income_CO_counties_2018_viz<-
median_income_CO_2018 %>%
mutate(County_Name=str_remove_all(NAME,"County, Colorado")) %>%
ggplot(aes(x=median_income,y=reorder(County_Name, median_income)))+
geom_errorbarh(aes(xmin = median_income - moe, xmax = median_income + moe)) +
geom_point(color = "blue", size = 3)+
labs(title="Median Income in Colorado, by County (2018)",
y="", x="Median Income Estimate from 5 year ACS\n(Bars indicate margin of error)")+
theme(plot.title=element_text(hjust=0.5))median_income_CO_counties_2018_viz 
12.3 Writing functions to visualize ACS data
county_median_income<-function(state_abbrev, state_name){
median_income<-get_acs(geography="county",
state=state_abbrev,
variables="B19013_001",
year=2018) %>%
rename(median_income=estimate) %>%
arrange(desc(median_income))
string_to_remove<-paste0("County", ", ", state_name)
chart_title<-paste0("Median Income in ", state_name, ", by County (2018)")
median_income_viz<-median_income %>%
mutate(County_Name=str_remove_all(NAME,string_to_remove)) %>%
ggplot(aes(x=median_income,y=reorder(County_Name, median_income)))+
geom_errorbarh(aes(xmin = median_income - moe, xmax = median_income + moe)) +
geom_point(color = "blue", size = 3)+
labs(title=chart_title,
y="", x="Median Income Estimate from 5 year ACS\n(Bars indicate margin of error)")+
theme(plot.title=element_text(hjust=0.5))+
scale_x_continuous(labels=scales::comma)
return(median_income_viz)
}# test function
county_median_income("NY", "New York")## Getting data from the 2014-2018 5-year ACS## Using FIPS code '36' for state 'NY'
# Creates vector of state abbreviations
state_abbrev_vector<-c("NM", "UT", "AZ", "WY")
# Creates vector of state names
state_name_vector<-c("New Mexico", "Utah", "Arizona", "Wyoming")
# Iteratively applies "county_median_income" function to inputs specified by "state_abbrev" vector
# and "state_name" vector, and assigns resulting visualization outputs to list named "median_income_2018_viz_list"
median_income2018_viz_list<-map2(state_abbrev_vector, state_name_vector, county_median_income)## Getting data from the 2014-2018 5-year ACS## Using FIPS code '35' for state 'NM'## Getting data from the 2014-2018 5-year ACS## Using FIPS code '49' for state 'UT'## Getting data from the 2014-2018 5-year ACS## Using FIPS code '04' for state 'AZ'## Getting data from the 2014-2018 5-year ACS## Using FIPS code '56' for state 'WY'# Creates a vector of names for "median_income2018_viz_list" list elements
names_median_income_list<-paste0(state_abbrev_vector, "_", "2018_ACS_medianincome")
# Inspect names in "names_median_income_list" vector
names_median_income_list## [1] "NM_2018_ACS_medianincome" "UT_2018_ACS_medianincome" "AZ_2018_ACS_medianincome"
## [4] "WY_2018_ACS_medianincome"# Assigns names in
names(median_income2018_viz_list)<-names_median_income_listmedian_income2018_viz_list## $NM_2018_ACS_medianincome
##
## $UT_2018_ACS_medianincome
##
## $AZ_2018_ACS_medianincome
##
## $WY_2018_ACS_medianincome
12.4 Dynamic mapping
median_income_2016_states<-get_acs(geography="state",
variables="B19013_001",
year=2016,
geometry=TRUE) %>%
rename(median_income=estimate) %>%
arrange(desc(median_income)) %>%
relocate(NAME)## Getting data from the 2012-2016 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.median_income_map<-tm_shape(median_income_2016_states)+
tm_polygons(col="median_income", n=6, style="fisher", palette="YlOrBr")
tmap_mode("view")## tmap mode set to interactive viewingmedian_income_map